BackgroundSARS-CoV-2 infection of the respiratory system can progress to a multi-systemic disease with aberrant inflammatory response. Cellular senescence promotes chronic inflammation, named as senescence-associated secretory phenotype (SASP). We investigated whether COVID-19 disease is associated with cellular senescence and SASP.MethodsAutopsy lung tissue samples from 11 COVID-19 patients and 43 age-matched non-COVID controls with similar comorbidities were analysed by immunohistochemistry for SARS-CoV-2, markers of senescence and key SASP cytokines. Virally-induced senescence was functionally recapitulated in vitro, by infecting epithelial Vero-E6 cells and a three-dimensional alveosphere system of alveolar type 2 (AT2) cells with SARS-CoV-2 strains isolated from COVID-19 patients.ResultsSARS-CoV-2 was detected by immunocytochemistry and electron microscopy predominantly in AT2 cells. Infected AT2 cells expressed the angiotensin-converting-enzyme 2 (ACE2) and exhibited increased senescence (p16INK4A and SenTraGorTM positivity) and IL-1β and IL-6 expression. In vitro, infection of Vero-E6 cells with SARS-CoV-2 induced senescence (SenTraGorTM), DNA damage (γ-H2AX) and increased cytokine (IL-1β, IL-6, CXCL8) and Apolipoprotein B mRNA-editing (APOBEC) enzyme expression. Next-generation-sequencing analysis of progenies obtained from infected/senescent Vero-E6 cells demonstrated APOBEC-mediated SARS-CoV-2 mutations. Dissemination of the SARS-CoV-2-infection and senescence was confirmed in extra-pulmonary sites (kidney and liver) of a COVID-19 patient.ConclusionsWe demonstrate that in severe COVID-19, AT2 cells infected by SARS-CoV-2 exhibit senescence and a proinflammatory phenotype. In vitro, SARS-CoV-2 infection induces senescence and inflammation. Importantly, infected senescent cells may act as a source of SARS-CoV-2 mutagenesis mediated by APOBEC enzymes. Therefore, SARS-CoV-2-induced senescence may be an important molecular mechanism of severe COVID-19, disease persistence and mutagenesis.
Alterations in the functions of neuronal RNA-binding proteins (RBPs) can contribute to neurodegenerative diseases. However, neurons also express a set of widely distributed RBPs that may have developed specialized functions. Here, we show that the ubiquitous member of the otherwise neuronal Elavl/Hu family of RNA-binding proteins, Elavl1/HuR, has a neuroprotective role. Mice engineered to lack exclusively HuR in the hippocampal neurons of the central nervous system (CNS), maintain physiologic levels of neuronal Elavls and develop a partially diminished seizure response following strong glutamatergic excitation; however, they display an exacerbated neurodegenerative response subsequent to the initial excitotoxic event. This response was phenocopied in hippocampal cells devoid of ionotropic glutamate receptors in which the loss of HuR results in enhanced mitochondrial dysfunction, oxidative damage and programmed necrosis solely after glutamate challenge. The molecular dissection of HuR and nElavl mRNA targets revealed the existence of a HuR-restricted posttranscriptional regulon that failed in HuR-deficient neurons and is involved in cellular energetics and oxidation defense. Thus, HuR acts as a specialized controller of oxidative metabolism in neurons to confer protection from neurodegeneration.
The cellular response to genotoxic stress is mediated by a well-characterized network of DNA surveillance pathways. The contribution of post-transcriptional gene regulatory networks to the DNA damage response (DDR) has not been extensively studied. Here, we systematically identified RNA-binding proteins differentially interacting with polyadenylated transcripts upon exposure of human breast carcinoma cells to ionizing radiation (IR). Interestingly, more than 260 proteins, including many nucleolar proteins, showed increased binding to poly(A) + RNA in IR-exposed cells. The functional analysis of DDX54, a candidate genotoxic stress responsive RNA helicase, revealed that this protein is an immediate-to-early DDR regulator required for the splicing efficacy of its target IR-induced pre-mRNAs. Upon IR exposure, DDX54 acts by increased interaction with a well-defined class of pre-mRNAs that harbor introns with weak acceptor splice sites, as well as by proteinprotein contacts within components of U2 snRNP and spliceosomal B complex, resulting in lower intron retention and higher processing rates of its target transcripts. Because DDX54 promotes survival after exposure to IR, its expression and/or mutation rate may impact DDR-related pathologies. Our work indicates the relevance of many uncharacterized RBPs potentially involved in the DDR.
Cardiovascular diseases (CVDs) constitute the prime cause of global mortality with an immense impact on patient quality of life and disability. Clinical evidence has revealed a strong connection between cellular senescence and worse cardiac outcomes in the majority of CVDs concerning both ischemic and non-ischemic cardiomyopathies. Cellular senescence is characterized by cell cycle arrest accompanied by alterations in several metabolic pathways, resulting in morphological and functional changes. Metabolic rewiring of senescent cells results in marked paracrine activity, through a unique secretome, often exerting deleterious effects on neighboring cells. Here, we recapitulate the hallmarks and key molecular pathways involved in cellular senescence in the cardiac context and summarize the different roles of senescence in the majority of CVDs. In the last few years, the possibility of eliminating senescent cells in various pathological conditions is being increasingly explored, giving rise to the field of senotherapeutics. Therefore, we additionally attempt to clarifythe current state of this field with a focus on cardiac senescence, and discuss the potential of implementing senolytics as atreatment option in heart disease.
Simulation of biomolecular networks with thousands of reactions is becoming essential for systems biology. We are presenting the design of a scalable System on Chip parallel architecture that implements Gillespie's First Reaction Method in reconfigurable FPGA hardware. Our SoC architecture can deliver performance (Mega-Reactions/sec) and throughput (MReaction cycles/sec) that is increasing linearly with the number of processors when simulating large biomolecular networks with up to m = 4096 reactions using a moderate size FPGA. We have synthesized and verified various SoC instances with up to N=8 Processing Elements for Xilinx Virtex 5 and Altera Cyclone III FPGAs, reaching clock frequencies up to 180 MHz and delivering simulation performance that is more than 2 order of magnitude higher than that of Intel Core 2 and i7 CPUs running at frequencies above 2GHz.
In this review, the fundamental basis of machine learning (ML) and data mining (DM) are summarized together with the techniques for distilling knowledge from state-of-theart omics experiments. This includes an introduction to the basic mathematical principles of unsupervised/supervised learning methods, dimensionality reduction techniques, deep neural networks architectures and the applications of these in bioinformatics. Several case studies under evaluation mainly involve next generation sequencing (NGS) experiments, like deciphering gene expression from total and single cell (scRNAseq) analysis; for the latter, a description of all recent artificial intelligence (AI) methods for the investigation of cell sub-types, biomarkers and imputation techniques are described. Other areas of interest where various ML schemes have been investigated are for providing information regarding transcription factors (TF) binding sites, chromatin organization patterns and RNA binding proteins (RBPs), while analyses on RNA sequence and structure as well as 3D dimensional protein structure predictions with the use of ML are described. Furthermore, we summarize the recent methods of using ML in clinical oncology, when taking into consideration the current omics data with pharmacogenomics to determine personalized 605 This article is freely accessible online.
We present SysPy (System Python) a tool which exploits the strengths of the popular Python scripting language to boost design productivity of embedded System on Chips for FPGAs. SysPy acts as a "glue" software between mature HDLs, ready-to-use VHDL components and programmable processor soft IP cores. SysPy can be used to: (i) automatically translate hardware components described in Python into synthesizable VHDL, (ii) capture top-level structural descriptions of processor-centric SoCs in Python, (iii) implement all the steps necessary to compile the user's C code for an instruction set processor core and generate processor specific Tcl scripts that import to the design project all the necessary HDL files of the processor's description and instantiate/connect the core to other blocks in a synthesizable top-level Python description. Moreover, we have developed a Hardware Abstraction Layer (HAL) in Python which allows user applications running in a host PC to utilize effortlessly the SoC's resources in the FPGA. SysPy's design capabilities, when complemented with the developed HAL software API, provide all the necessary tools for hw/sw partitioning and iterative design for efficient SoC's performance tuning. We demonstrate how SysPy's design flow and functionalities can be used by building a processor-centric embedded SoC for computational systems biology. The designed SoC, implemented using a Xilinx Virtex-5 FPGA, combines the flexibility of a programmable soft processor core (Leon3) with the high performance of an application specific core to simulate flexibly and efficiently the stochastic behavior of large size biomolecular reaction networks. Such networks are essential for studying the dynamics of complex biological systems consisting of multiple interacting pathways.
Circular RNAs (circRNA) comprise a distinct class of non-coding RNAs that are abundantly expressed in the cell. CircRNAs have the capacity to regulate gene expression by interacting with regulatory proteins and/or other classes of RNAs. While a vast number of circRNAs have been discovered, the majority still remains poorly characterized. Particularly, there is no detailed information on the identity and functional role of circRNAs that are transcribed from genes encoding components of the DNA damage response and repair (DDRR) network. In this article, we not only review the available published information on DDRR-related circRNAs, but also conduct a bioinformatic analysis on data obtained from public repositories to uncover deposited, yet uncharacterized circRNAs derived from components of the DDRR network. Finally, we interrogate for potential targets that are regulated by this class of molecules and look into potential functional implications.
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